How to save and load experiment/model from `optimize`

Hi, from the documentation, the optimize function returns the (best_parameters, values, experiment, model) tuple. I’m wondering what’s the best practices for saving these values (e.g. for visualization in a different machine)? Also, is it possible to interrupt a model and later resume from the state in the optimize API? Thanks!

[lena-kashtelyan]

Hi, @ksanjeevan, @theinlinaung2010! Sorry to have missed your tags, the issue was closed so we weren’t notified in time. Feel free to reopen the issue if there is a follow-up, in the future, to make sure we get back to you as soon as we can!

For 2), you can use get_best_parameters utility to find the best point on the experiment. You can also use exp_to_df to view the trials in your experiment as a convenient dataframe. Let me know if those don’t fully address your concern!

To use storage functionality for experiment and models used in optimization, I would recommend using our Service API that is well integrated with our storage layer (you can store locally to a .json file or to an SQL backend). You can check out our API comparison code snippets and the Service API tutorial to see how to get started and how to leverage storage.

Let us know if you have further questions, I’ll keep the issue open for now.

[ldworkin]

Hi @nwrim ! The easiest way to accomplish what you want is probably to save the data that you’re using to fit the model (rather than the model itself), and then you can refit it whenever you want. If you want to take advantage of Ax’s storage to do so, you would use experiment.attach_data() followed by save_experiment. Then you can use load_experiment and experiment.lookup_data_for_trial or experiment.lookup_data_for_ts at a later point to grab that data again and refit the model. Does that sound like it would help?